153241-Thumbnail Image.png
Description
Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal

visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.

The

Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal

visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.

The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.

In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images

acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new

similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector

exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
Reuse Permissions


  • Download restricted.

    Details

    Title
    • Spatial and multi-temporal visual change detection with application to SAR image analysis
    Contributors
    Date Created
    2014
    Resource Type
  • Text
  • Collections this item is in
    Note
    • thesis
      Partial requirement for: Ph.D., Arizona State University, 2014
    • bibliography
      Includes bibliographical references (p. 107-120)
    • Field of study: Electrical engineering

    Citation and reuse

    Statement of Responsibility

    by Qian Xu

    Machine-readable links